 ## Token Efficiency: How GitHub is Rewriting the Rules for AI Agents Developers working with AI agents know the main pain point: every request costs tokens, and tokens mean money. The more tokens an agent consumes, the higher the costs and the slower it operates. The GitHub Blog recently published an article on how they are solving this problem in their agentic workflows. ### What Changed? The key idea is token optimization at the agent architecture level. Instead of passing the entire context between steps, GitHub has implemented mechanisms that: — Reduce token consumption by 30-50% through smart caching of intermediate results — Speed up the execution of action chains—the agent does not reload the entire context at each step — Lower inference costs—fewer tokens mean lower API expenses ### Why Is This Important for Developers? For those building agentic systems, token efficiency is the difference between a prototype and production. If each agent run costs pennies instead of dollars, you can afford more iterations, more tests, and more features. GitHub shows that the right agent architecture is not just about response quality but also about economics. In an era where AI agents are becoming a primary developer tool, token efficiency takes center stage. ### What to Do? For small teams and solo developers, this is especially critical. Token optimization allows running complex agentic pipelines without needing to rent expensive GPU servers. Businesses with up to 50 employees can adopt AI agents without a surge in cloud costs. Original article: [Improving token efficiency in GitHub Agentic Workflows](https://github.blog/ai-and-ml/github-copilot/improving-token-efficiency-in-github-agentic-workflows/)